When sailing at sea, the smart ship will inevitably produce swaying motion due to the action of wind, wave and current, which makes the image collected by the visual sensor appear motion blur. This will have an adverse effect on the object detection algorithm based on the vision sensor, thereby affect the navigation safety of the smart ship. In order to remove the motion blur in the images during the navigation of the smart ship, we propose SharpGAN, a new image deblurring method based on the generative adversarial network. First of all, the Receptive Field Block Net (RFBNet) is introduced to the deblurring network to strengthen the network's ability to extract the features of blurred image. Secondly, we propose a feature loss that combines different levels of image features to guide the network to perform higher-quality deblurring and improve the feature similarity between the restored images and the sharp image. Finally, we propose to use the lightweight RFB-s module to improve the real-time performance of deblurring network. Compared with the existing deblurring methods on large-scale real sea image datasets and large-scale deblurring datasets, the proposed method not only has better deblurring performance in visual perception and quantitative criteria, but also has higher deblurring efficiency.
Complex marine environment has an adverse effect on the object detection algorithm based on the vision sensor for the smart ship sailing at sea. In order to eliminate the motion blur in the images during the navigation of the smart ship and ensure safety, we propose SharpGAN, a new image deblurring method based on the generative adversarial network (GAN). First of all, we introduce the receptive field block net (RFBNet) to the deblurring network to enhance the network’s ability to extract blurred image features. Secondly, we propose a feature loss that combines different levels of image features to guide the network to perform higher-quality deblurring and improve the feature similarity between the restored images and the sharp images. Besides, we use the lightweight RFB-s module to significantly improve the real-time performance of the deblurring network. Compared with the existing deblurring methods, the proposed method not only has better deblurring performance in subjective visual effects and objective evaluation criteria, but also has higher deblurring efficiency. Finally, the experimental results reveal that the SharpGAN has a high correlation with the deblurring methods based on the physical model.
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